UPdated

api.py

@@ -323,35 +323,24 @@ def perform_sentiment_analysis(news_data):
     return news_data, sentiment_counts
 
 def extract_topics_with_hf(news_data):
+    from transformers import pipeline
     structured_data = {
         "Company": news_data.get("Company", "Unknown"),
         "Articles": []
     }
+    topic_pipe = pipeline("text-classification", model="valurank/distilroberta-topic-classification",device=1)
     articles = news_data.get("Articles", [])
     for article in articles:
         content = f"{article['Title']} {article['Summary']}"
-        # Define the prompt for Groq AI
-        prompt = f"""
-        Analyze the following content: "{content}"
-        Extract and return **exactly three key topics** most relevant to this content. 
-        The topics should be of one word after analyzing the content.
-        Respond in a JSON format like this:
-        {{"Topics": ["topic1", "topic2", "topic3"]}}
-        """
-        try:
-            # Use Groq AI to invoke the model
-            response = llm.invoke([HumanMessage(content=prompt)]).content
-            topics_result = json.loads(response).get("Topics", ["Unknown"])  # Parse JSON response
-        except Exception as e:
-            print(f"Error while extracting topics: {e}")
-            topics_result = ["Unknown"]
+        topics_result = topic_pipe(content, top_k=3)
+        topics = [topic["label"] for topic in topics_result] if topics_result else ["Unknown"]
 
         structured_data["Articles"].append({
             "Title": article["Title"],
             "Summary": article["Summary"],
             "Sentiment": article.get("Sentiment", "Unknown"),
             "Score": article.get("Score", 0.0),
-            "Topics": topics_result
+            "Topics": topics
         })
     return structured_data
 
@@ -493,7 +482,7 @@ def generate_summary(company_name):
 def home():
     return {"message": "Welcome to the Company Sentiment API"}
 
-@app.get("/generateSummary")
+@app.post("/generateSummary")
 def get_summary(company_name: str = Query(..., description="Enter company name")):
     structured_summary = generate_summary(company_name)
     return structured_summary